Machine learning has made a significant practical impact in a vast array of disciplines, ranging from healthcare to defense to finance. This presentation will be focused on healthcare and will describe how machine learning techniques may be applied to improve patient outcomes in three different clinical applications, including: the reduction of false cardiac arrhythmia alarms in the intensive care unit (ICU), the prediction of acute hypotensive episodes in the ICU, and the automated classification of heart sounds using phonocardiography. Each of these research topics presents its own unique challenges that influence the selection of optimal machine learning algorithms. Machine learning techniques have been shown to enhance patient outcomes in not only these three applications, but also in a wide variety of additional clinical scenarios. The results underscore the value of data in making more informed patient care decisions and also help to demonstrate the wide applicability of machine learning algorithms.

Bio: Grace is a graduate of the Benedictine University-Illinois Institute of Technology Engineering program, earning a B.S. in Computer Science and a B.S. in Computer Engineering in 2004. She was awarded a National Science Foundation Graduate Research Fellowship for her graduate studies at Northwestern University, where she earned a M.S. (2006) and Ph.D. (2008) in Electrical Engineering. Prior to joining Benedictine in Fall 2014, Grace worked as a Signal & Image Processing Engineer at Northrop Grumman Corporation. She is currently an Assistant Professor in Computer Science, with research interests in machine learning and computer vision.